1. Field of the Invention
This disclosure is related to the field of object detection, and more particularly to systems and methods for detecting the presence of a biological mass within a wireless communications network.
2. Description of the Related Art
Tracking objects may be done using a number of techniques. For example, a moving transceiver may be attached to the object. Examples of such systems include global positioning location systems such as GPS, which use orbiting satellites to communicate with terrestrial transceivers. However, such systems are generally less effective indoors, where satellite signals may be blocked, reducing accuracy. Thus, other technologies are often used indoors, such as Bluetooth™ beacons, which calculate the location of a roaming or unknown transceiver. The roaming transceiver acts as a fiducial element.
These systems have several disadvantages, among them that the object tracked must include a transceiver. In certain applications, the object to be tracked will have no such fiducial element, or will actively disable any such element, such as an intruder in a home.
Other technologies exist which can also detect and track objects without the use of a fiducial element. For example, radar is a venerable object-detection system that uses RF waves to determine the range, angle, or velocity of objects, including aircraft, ships, spacecraft, guided missiles, motor vehicles, weather formations, and terrain. Radar operates by transmitting electromagnetic waves, generally using waves in the radio frequency (“RF”) of the electromagnetic spectrum, which reflect from any object in their path. A receiver, typically part of the same system as the transmitter, receives and processes these reflected waves to determine properties of the objects. Other systems similar to radar, using other parts of the electromagnetic spectrum, may also be used in similar fashion, such as ultraviolet, visible, or near-infrared light from lasers.
Radar technologies do not require a fiducial element, but have other shortcomings. For example, radar signals are susceptible to signal noise, or random variations in the signal caused by internal electrical components, as well as noise and interference from external sources, such as the natural background radiation. Radar is also vulnerable to external interference sources, such as intervening objects blocking the beam path and can be deceived by objects of particular size, shape, and orientation.
In addition, the growth of home automation technologies has provided a wealth of additional interaction vectors for “Internet of Things” (“IoT”) devices, including voice recognition for voice-based control. This technology has been proliferated by various commercial enterprises such as Amazon (Alexa), Google (Google Home), Apple (Siri), and Microsoft (Cortana). These voice recognition systems are currently used with IOT devices to provide users with the ability to provide spoken word input to IOT devices, instructing them to perform certain functions. Currently, this interaction is typically one-directional, with the voice control system sending commands to the IoT device and receiving status updates from those devices when queried by the voice control system. The market lacks third-party systems which trigger voice recognition systems, such as to prompt a response.